Abstract—In this study, Artificial Neural Networks (ANN)
and Artificial Immune (AI) techniques designed in the form of
a hybrid structure are used for diagnosis of epilepsy patients
via EEG signals. Attributes of EEG signals are needed to be
determined by employing EEG signals which are recorded
using EEG. In this process the raw digital signals data is
received and is summarized in some respects. From this data,
four characteristics are extracted for the classification process.
20% of available data is reserved for testing while 80% of
available data is being reserved for training. These actions
were repeated five times by performing cross-validation
process. AIS is used for updating the weights during training
ANN and a program is constituted for the classification of EEG
signals. Education and recording processes were performed
with different parameters by means of the constituted program.
The obtained findings show that the proposed method was
effective for achieving accurate results as much as possible
with the use of ANN and AIS, together.
Index Terms—Artificial neural network, artificial immune
systems, clonal selection, epilepsy, EEG signals.
Sema Arslan is with the Department of Computer Engineering, Faculty
of Engineering and Architecture, Selcuk University (e-mail:
semaarslan@selcuk.edu.tr).
Hakan Işik is with the Department of Electronic and Computer
Education, Faculty of Technical Education, Selcuk University, 42079,
Konya-Turkey (e-mail: hisik@selduk.edu.tr).
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Cite:Sema Arslan and Hakan Işik, "The Hybrid Classification Model Thanks to Artificial Neural Network and Artificial Immune Systems for Diagnosis of Epilepsy from Electroencephalography," Journal of Advances in Computer Networks vol. 2, no. 1, pp. 31-34, 2014.
